Is increased mortality by multiple exposures to COVID-19 an overseen factor when aiming for herd immunity?

A flexible age-dependent, spatially-stratified predictive model for the spread of COVID-19, accounting for multiple viral variants and vaccines

BACKGROUND

After COVID-19 vaccines received approval, vaccination campaigns were launched worldwide. Initially, these were characterized by a shortage of vaccine supply, and specific risk groups were prioritized. Once supply was guaranteed and vaccination coverage saturated, the focus shifted from risk groups to anti-vaxxers, the under-aged population, and regions of low coverage. At the same time, hopes to reach herd immunity by vaccination campaigns were put into perspective by the emergence and spread of more contagious and aggressive viral variants. Particularly, concerns were raised that not all vaccines protect against the new-emerging variants. The objective of this study is to introduce a predictive model to quantify the effect of vaccination campaigns on the spread of SARS-CoV-2 viral variants.

METHODS AND FINDINGS

The predictive model introduced here is a comprehensive extension of the one underlying the pandemic preparedness tool CovidSim 2.0 (http://covidsim.eu/). The model is age and spatially stratified, incorporates a finite (but arbitrary) number of different viral variants, and incorporates different vaccine products. The vaccines are allowed to differ in their vaccination schedule, vaccination rates, the onset of vaccination campaigns, and their effectiveness. These factors are also age and/or location dependent. Moreover, the effectiveness and the immunizing effect of vaccines are assumed to depend on the interaction of a given vaccine and viral variant. Importantly, vaccines are not assumed to immunize perfectly. Individuals can be immunized completely, only partially, or fail to be immunized against one or many viral variants. Not all individuals in the population are vaccinable. The model is formulated as a high-dimensional system of differential equations, which is implemented efficiently in the programming language Julia. As an example, the model was parameterized to reflect the epidemic situation in Germany until November 2021 and future dynamics of the epidemic under different interventions were predicted. In particular, without tightening contact reductions, a strong epidemic wave is predicted during December 2021 and January 2022. Provided the dynamics of the epidemic in Germany, in late 2021 administration of full-dose vaccination to all eligible individuals (e.g. by mandatory vaccination) would be too late to have a strong effect on reducing the number of infections in the fourth wave in Germany. However, it would reduce mortality. An emergency brake, i.e., an incidence-based stepwise lockdown, would be efficient to reduce the number of infections and mortality. Furthermore, to specifically account for mobility between regions, the model was applied to two German provinces of particular interest: Saxony, which currently has the lowest vaccine rollout in Germany and high incidence, and Schleswig-Holstein, which has high vaccine rollout and low incidence.

CONCLUSIONS

A highly sophisticated and flexible but easy-to-parameterize model for the ongoing COVID-19 pandemic is introduced. The model is capable of providing useful predictions for the COVID-19 pandemic, and hence provides a relevant tool for epidemic decision-making. The model can be adjusted to any country, and the predictions can be used to derive the demand for hospital or ICU capacities.

Predicting the combined effects of case isolation, safe funeral practices, and contact tracing during Ebola virus disease outbreaks

BACKGROUND

The recent outbreaks of Ebola virus disease (EVD) in Uganda and the Marburg virus disease (MVD) in Ghana reflect a persisting threat of Filoviridae to the global health community. Characteristic of Filoviridae are not just their high case fatality rates, but also that corpses are highly contagious and prone to cause infections in the absence of appropriate precautions. Vaccines against the most virulent Ebolavirus species, the Zaire ebolavirus (ZEBOV) are approved. However, there exists no approved vaccine or treatment against the Sudan ebolavirus (SUDV) which causes the current outbreak of EVD. Hence, the control of the outbreak relies on case isolation, safe funeral practices, and contact tracing. So far, the effectiveness of these control measures was studied only separately by epidemiological models, while the impact of their interaction is unclear.

METHODS AND FINDINGS

To sustain decision making in public health-emergency management, we introduce a predictive model to study the interaction of case isolation, safe funeral practices, and contact tracing. The model is a complex extension of an SEIR-type model, and serves as an epidemic preparedness tool. The model considers different phases of the EVD infections, the possibility of infections being treated in isolation (if appropriately diagnosed), in hospital (if not properly diagnosed), or at home (if the infected do not present to hospital for whatever reason). It is assumed that the corpses of those who died in isolation are buried with proper safety measures, while those who die outside isolation might be buried unsafely, such that transmission can occur during the funeral. Furthermore, the contacts of individuals in isolation will be traced. Based on parameter estimates from the scientific literature, the model suggests that proper diagnosis and hence isolation of cases has the highest impact in reducing the size of the outbreak. However, the combination of case isolation and safe funeral practices alone are insufficient to fully contain the epidemic under plausible parameters. This changes if these measures are combined with contact tracing. In addition, shortening the time to successfully trace back contacts contribute substantially to contain the outbreak.

CONCLUSIONS

In the absence of an approved vaccine and treatment, EVD management by proper and fast diagnostics in combination with epidemic awareness are fundamental. Awareness will particularly facilitate contact tracing and safe funeral practices. Moreover, proper and fast diagnostics are a major determinant of case isolation. The model introduced here is not just applicable to EVD, but also to other viral hemorrhagic fevers such as the MVD or the Lassa fever.

Clinico–Epidemio-Microbiological Exploratory Review Among COVID-19 Patients with Secondary Infection in Central India

Purpose

Secondary infections (SI) in COVID-19 have been documented from 3.6% to 72% in various studies with mortality ranging from 8.1% to 57.6%. There is a gap in knowledge for clinico–epidemio-microbilogical association among COVID-19 patients with concomitant SI.

Patients and Methods

This is a retrospective chart review, in central India. The study was undertaken for hospitalized adult patients during 1st June 2020 to 30th November 2020, with laboratory proven COVID-19 infection and secondary infection.

Results

Out of the total 2338 number of patients, only 265 (11.3%) patients were investigated for microbiological identification of SI. Male gender was predominant (76.8%) and the mean age was 53.7 ± 17.8 years. Only 3.5% (82/2338) of patients were having microbiologically confirmed (bacterial or fungal) SI. The overall mortality was 50.9% (54/82) with a differential mortality of 88.8% (48/54) in high-priority areas and 21.4% (6/28) in low-priority areas. Blood was the most commonly investigated sample (56%) followed by urine (20.7%) and respiratory secretion (15.8%). A. baumanii complex (20/82, 24.3%) was the most common bacteria isolated followed by K. pneumonia (12/82, 14.6%) and E. coli (11/82, 13.4%). Candida spp. (20/82, 24.3%) was the most common fungal pathogen isolated. Sixty percent (12/20) of Acinetobacter spp. were carbapenam-resistant and 70.3% of Enterobacterales were carbapenam-resistant. Fluconazole resistant Candida spp. was isolated only in 10% (2/20) of cases. Diabetes was the most common co-morbidity 54.8% (45/82) followed by hypertension (41.4%) and chronic heart disease (13.4%). The negative predictors of secondary infections are urinary catheterization, placement of central line and mechanical ventilation (invasive and non-invasive).

Conclusion

There is an urgent need of better anti-microbial stewardship practices in India (institutional and extra institutional) for curtailment of secondary infection rates particularly among COVID-19 patients.

The heterogeneous regional effect of mobility on Coronavirus spread

The Coronavirus (COVID-19) pandemic struck global society in 2020. The pandemic required the adoption of public policies to control spread of the virus, underlining the mobility restrictions. Several studies show that these measures have been effective. Within the topic of Coronavirus spread, this original paper analyses the effect of mobility on Coronavirus spread in a heterogeneous regional context. A multiple dynamic regression model is used to control sub-national disparities in the effect of mobility on the spread of the Coronavirus, as well as to measure it at the context of Spanish regions. The model includes other relevant explanatory factors, such as wind speed, sunshine hours, vaccinated population and social awareness. It also develops a new methodology to optimise the use of Google trends data. The results reveal heterogeneity among regions, which has important implications for current and future pandemic containment strategies.

Complications Post-COVID-19 and Risk Factors among Patients after Six Months of a SARS-CoV-2 Infection: A Population-Based Prospective Cohort Study

In October 2020, we conducted a population-based prospective cohort study to determine post-COVID-19 complications, recovery, return to usual health, and associated risk factors in 536 cases of COVID-19 outbreak in Borriana (Spain) by administering an epidemiological questionnaire via phone interviews. A total of 484 patients participated (90.3%), age mean 37.2 ± 17.1 years, and 301 females (62.2%). Mild illness was the most common COVID-19 manifestation. After six months, 160 patients (33.1%) suffered at least one complication post-COVID-19, and 47 (29.4%) of them sought medical assistance. The most frequent persistent symptoms were hair loss, fatigue, loss of smell or taste, and headache. Risk factors associated with a complication were female sex (adjusted relative risk, [aRR] = 1.93 95% confidence interval [CI] 1.41-2.65), age 35 years and above (aRR = 1.50 95% CI 1.14-1.99), B blood group (aRR = 1.51 95% CI 1.04-2.16), current smoker (RR = 1.61 95% CI 1.02-2.54), and at least a COVID-19 exposure (aRR = 2.13 95% CI 1.11-4.09). Male sex, age younger than 35 years, and low COVID-19 exposures were associated with better recovery and return to usual health. A third of patients presented persistent symptoms compatible with the long-COVID-19 syndrome. In conclusion, an active medical follow-up of post-COVID-19 patients must be implemented.